Bibliographic record
Abstract
Imagine you've been elected to the House of Commons.You'd been politically active in your hometown but hadn't thought of running for Parliament until friends urged you to go for it.It was a long shot; you never thought you'd win but you did.You've packed your bags and found your way to Ottawa (which you had never visited before).And here you are.You've had many tasks since election day.You've had to find a place to live in Ottawa during each session (something members from ridings around Ottawa don't need to worry about), move into the parliamentary office assigned to you, start recruiting staff, and establish a constituency office (unless you can take over the outgoing member's office).House Administration organized a day-long orientation session that helped a bit, providing mostly administrative, financial, and legal details relating to the institutional organization of the House, staffing your offices, and managing your office budget as well as enlisting some veteran members to sit on panels to talk about the ups and downs of the parliamentary life you're about to begin.You may have found this somewhat overwhelming: too much information too soon.A House Administration staffer has been assigned to assist you in getting organized before the opening of Parliament in a few weeks.You attended your first party caucus -little more than a hello-howare-you gathering -where you met fellow party members from parts of Canada you'd never visited.You're soon looking forward to getting back home and you hope to get back before returning to Ottawa for the opening of the new Parliament.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.734 | 0.576 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".